PostSV: A Post-Processing Approach for Filtering Structural Variations.

ABSTRACT: Genomic structural variations are significant causes of genome diversity and complex diseases. With advances in sequencing technologies, many algorithms have been designed to identify structural differences using next-generation sequencing (NGS) data. Due to repetitions in the human genome and the short reads produced by NGS, the discovery of structural variants (SVs) by state-of-the-art SV callers is not always accurate. To improve performance, multiple SV callers are often used to detect variants. However, most SV callers suffer from high false-positive rates, which diminishes the overall performance, especially in low-coverage genomes. In this article, we propose a post-processing classification-based algorithm that can be used to filter structural variation predictions produced by SV c

SUBMITTER: Alzaid E 

PROVIDER: S-EPMC6974750 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature